Jorge Mauricio Celis Moreno

GROWTH AND MATURATION IN MALE ADOLESCENT

TENNIS PLAYERS: agreement between protocols to estimate skeletal age

Dissertação de Mestrado em Treino Desportivo para Crianças e Jovens,

apresentada à Faculdade de Ciências do Desporto e Educação Física da

Universidade de Coimbra

Janeiro 2017

ii

UNIVERSITY OF COIMBRA Faculty of Sport Sciences and Physical Education

GROWTH AND MATURATION IN MALE ADOLESCENT TENNIS PLAYERS:

agreement between protocols to estimate skeletal age

JORGE MAURICIO CELIS MORENO

January, 2017

Dissertation submitted to the Faculty of

Sport Sciences and Physical Education of

the University of Coimbra, for the

degree of Master in Youth Sports

Training.

Supervisors:

Dr Manuel João Coelho e Silva

Dr Artur Romão Pereira

iii

Celis, J.M. (2017). Growth and maturation in male adolescent tennis players:

agreement between protocols to estimate skeletal age, dissertation for the degree

of Master in Youth Sports Training. University of Coimbra. Coimbra, Portugal.

iv

ACKNOWLEDGMENTS

I would like to thank professor Manuel João Coelho e Silva for supervising and

improved the quality of this work, for the scientific considerations that greatly

contributed to my professional growth.

Special thanks to Professor João Valente dos Santos, the family Rodriguez

Lancheros, Mrs. Lucero Obando, and my friend Matthieu Garcia for their help and

support in the difficult moments during the realization of my master degree and

my time in Coimbra..

Finally, to my sister Yineth Celis, brother Miller Celis, father Jorge Celis, mother

Marina Moreno, uncle Ricardo Celis and grandmother Leonor Rincon, who are my

closest family and they are always with me in the most important moments.

v

ABSTRACT

The aim of this study was to examine the agreement between concurrent protocols

for determination of skeletal age (FELS and TW3). The sample consisted of 80 male

adolescent tennis players aged 8.69-16.79, they were from Great Britain, Portugal

and Colombia. Standard hand wrist radiograph was taken to estimate skeletal age

with the protocols to Fels method (Roche et al.,1988) and TW3 method (Tanner et

al., 2001). Analysis of variance ANOVA was used to compare Fels and TW3 SAs in

players who were not classified as skeletally mature by either method.. The

difference between means of Fels and TW3 SAs were significant (F=37,50 ,

p<0,001). Fels method was a slight more advanced in the classification compared

with TW3.

A possible influence by variation in maturity status on body composition,

size and physique could determine the performance in adolescents tennis players

who needs speed, agility and aerobic capacity for the competition.

Keywords: Growth, Maturation, Skeletal age, Tennis, Adolescent.

vi

RESUMEN

El objetivo de este estudio fue examinar el grado de aceptación que existe entre

los protocolos para la determinación de la edad esquelética (FELS y TW3). La

muestra consistió en 80 jugadores de tenis adolescentes hombres de 8,69-16,79

años, procedentes de Gran Bretaña, Portugal y Colombia. Se tomaron radiografías

estándar de muñeca y mano para estimar la edad esquelética con los protocolos

del método de Fels (Roche et al., 1988) y el método TW3 (Tanner et al., 2001). El

análisis de varianza ANOVA se utilizó para comparar Fels y TW3 SAs en los

jugadores que no fueron clasificados como esqueléticamente maduros por ninguno

de los métodos. Las diferencias entre la media de Fels y TW3 SAs fueron

significativas (F = 37,50, p <0,001). El método Fels fue un poco más avanzado en la

clasificación de madurez comparado con la clasificación de TW3.

La posible influencia de la variación en el estado de madurez sobre la

composición corporal, el tamaño y el físico podría determinar el desempeño en los

jugadores de tenis adolescentes que necesitan de velocidad, agilidad y capacidad

aeróbica para la competencia.

Palabras clave: Crecimiento, maduración, edad esquelética, tenis, Adolescente.

vii

LIST OF TABLES

Table 1. Descriptive statistics for chronological age, stature of the father, stature of the mother and anthropometry (n=80)……………………………………..

24

Table 2. Descriptive statistics for maturity offset, estimated age at the peak height velocity, predicted mature stature (according to different concurrent) and skeletal aged determined by concurrent protocols (n=80).

25

Table 3. Skeletal maturity status* by different protocols and crosstab of absolute frequencies (n=80)……………………………………………………………………..

27

viii

LIST OF FIGURES

Figure 1. Linear relationship between stature and CA for the total sample (n=80)…………………………………………………………………………………………………..........

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Figure 2. Linear relationship between body Mass and CA for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 3. Linear relationship between Fels and CA for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 4. Linear relationship between TW3 and CA for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 5. Linear relationship between TW2 and CA for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 6. Linear relationship between TW3 and Fels for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 7. Linear relationship between TW2 and Fels for the total sample (n=80)………………………………………………………………………………………………………...

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Figure 8. Linear relationship between TW3 and TW2 for the total sample (n=80)………………………………………………………………………………………………………...

32

ix

LIST OF ABBREVIATIONS

ATP -Association of Tennis Professional

CA -Chronological age

Cm -Centimeter

GP -Greulich and Pyle

ITF -International Tennis Federation

Kg -Kilogram

K&G -Kamis and Guo

K&R -Kamis and Roche

LTAD -Long Term Athlete Development

RUS -Radius Ulna and Short bones

SA -Skeletal Age

TW3 - Tanner and Whitehouse Version 3

USA -United States of America

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TABLE OF CONTENTS

ACKNOWLEDGMENTS……………………………………………………………… x ABSTRACT……………………………………………………………………………… . x LIST OF TABLES……………………………………………………………………….. xii LIST OF FIGURES……………………………………………………………………… Viii LIST OF ABBREVIATIONS…………………………………………………………

Ix

CHAPTER 1: INTRODUCTION…………………………………………………..

11

1.1 Youth tennis…………………………………………………………………. 11 1.2 Growth and maturation………………………………………………… 13 1.3. Demands of game and Physical profile………………………….. 14 1.4. The young tennis player……………………………………………… 15 1.5. Skeletal age………………………………………………………………… 16 1.6. Research question…………………………………………………………

19

CHAPTER 2: METHODS ………………………………………………………….

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2.1. Procedures…………………………………………………………………… 20 2.2. Participants………………………………………………………………….. 20 2.3. Skeletal age………………………………………………………………….. 21 2.4. Skeletal maturation………………………………………………………. 21 2.5. Skeletal maturity………………………………………………………….. 21 2.6. Statistical analysis…………………………………………………………

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CHAPTER 3: RESULTS……………………………………………………………...

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CHAPTER 4: DISCUSSION…………………………………………………………

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4.1. Main findings………………………………………………………………... 33 4.2. Greulich and Pyle method……………………………………………... 33 4.3. Tanner and whitehouse method……………………………………. 34 4.4. Fels method…………………………………………………………………. 35 4.5. Agreement among protocols…………………………………………. 36 4.6. Growth status………………………………………………………………. 37 4.7. Maturity status…………………………………………………………….. 37 4.8. Interrelationship among indicators of biological

maturation…………………………………………………………………………

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CHAPTER 5: CONCLUSIONS……………………………………………………..

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CHAPTER 6: REFERENCES……………………………………………………..... 41

11

CHAPTER 1:

INTRODUCTION

Tennis is among of the most popular sports. According to International Tennis

Federation ITF more than 60 million people practice this sport and 200 countries

are members of ITF (Pluim et al., 2006) Hence, tennis is a sport extensively

studied. Most of the scientific literature focused on physiological (kovacs, 2006;

2007), biomechanical variables (Elliot et al., 2003), physical performance,

(Fernandez-Fernandez et al., 2006; 2015; 2016) and prevention and treatment of

injuries (Bylak & Hutchinson, 1998).

1.1. Youth tennis

In search of talent identification many authors describe characteristics of the

athletes in young ages, in an attempt to asses strengths and weaknesses attributes

of given player (Girard & Millet 2009; Sánchez-Muñoz et al., 2007). Prediction of

adult stature in young is important (Pearson et al., 2006). Boys advanced in terms

of maturity tended to be stronger and to perform better on motor performance

items that require strength compared to peers who were on time or late maturing

(Baxter-Jones et al., 1995; Malina et al., 2004). Successful youth tennis players are

characterized by speed, size, strength, and high skill (Fernandez et al., 2006) . The

belief that early identification of talent leads to improved performance has

12

resulted in formal talent identification beginning in late childhood and early

adolescence (Balyi & Williams, 2009).

The topic of talent identification, selection and development is central in

youth sports (Bompa 1995; Bohme, 2007; Weinek, 2005; Balyi & Williams, 2009)

which is a process that implies about 10- 12 years of regular practice and competition

(Platonov, 2001). With the aim the continuous improvement of training programs

Canada sports implemented the Long Term Athlete Development (LTAD) this is the

first program focused on the general framework of athlete development in a long

term having reference on growth, maturation, and development, which has like a

reference of biological maturation de Peak height velocity (Mirwald et al., 2002).

The LTAD is a approach that was implemented in Canada to every national sports

and was based on deferments studies of human development and the best sports

systems. LTAD has present the biological age vs. chronological age of participants

there by designing programs that according the needs of athletes at their stage of

development. LTAD has a systematic identification of stages and try to improve

the potential of development and increase participation of athletes. It provides a

better way for developing physical literacy, physical fitness and competitive ability,

using a steps order approach according the physical, mental and emotional

situations of each participant. Finally, LTAD is an important system that unites

athlete, coach, officials, administrators, parents and sports science, (Canada

sports, 2015).

13

1.2. Growth and maturation

Growth and maturation are two biological processes so important during the

period of formation of young athletes, from childhood to adolescence and from

adolescence to adulthood occur many transformations. These changes significantly

interact with trainability and readiness for sport participation, according to

(Malina et al., 2004; 2014; Baxter-Jones et al., 2002). Growth, maturation and

development are the major biological processes among the first two decades of life.

Growth refers to increase in body size, while maturation refers to progress toward

the mature state. Finally, development is mainly behavioral and comprises

qualitative changes in the adaptation of the individual to the ecological context.

These three processes are simultaneously and investigation in pediatric sports

science are emerging and still needed.

Maturation can be assessed by the most commonly used indicators, that is

by the appearance of secondary sex characteristics, such as pubic hair or genital

development. The somatic growth curve, for example maximum growth in height

during adolescent years is a non invasive alternative but demands longitudinal

data. Skeletal age obtained from the hand and wrist. (Malina et al., 2004; Baxter-

Jones et al., 2002) is a valid measurement and considered the best single criteria to

assess biological maturation (Malina et al. 2004).

14

Children of the same chronological age can vary considerably in biological maturity

status. Thus, variation among individuals in maturity status a given point in time

and in progress over time influences measure of growth and performance (Malina

et al., 2004). Variation in growth and performance associated the maturity status

with adolescence on tennis players is described in (Myburgh et al., 2016 a; b.).

1.3. Demands of game and Physical profile

Competitive tennis requires a fine interaction among tactical, technical,

psychological and physical attributes (Kovacs, 2007). Physiological demands of

competition depend of style and intensity of game, every competitive match can be

different duration (approximately 105 min) with a work-to-rest ratio of 1:2 to 1:3.

Real play time is about 31% of total play time (Torres-Luque et al., 2011). During

this time, a tennis player runs an average of 3 m per shot and a total of 8–12 m in

the course of a point, completing 300–500 high intensity efforts during a best of

three sets match. (Fernandez et al., 2006).

According to Fernandez et al., (2006) tennis is a intermittent sport of

maximal efforts 5-10s by rest time of 10-20s during a time of 1-5h, thus a tennis

match involves a combination of low and high intensity periods, and tennis can be

considered to be an intermittent anaerobic sport with an aerobic recovery phase.

For this reason competitive tennis players needs mixture of anaerobic (speed and

agility) combined with a aerobic capacity to competition (Kovacs, 2007).

15

According to Torres-Luque et al., (2011) the characteristics of competitions

in young tennis players is characterized by playing time around 105±20 min (real

play time of 32 minutes, and resting time of 74 minutes). The study showed that

the temporal structure of individual tennis play in adolescent players playing on a

hard surface is similar to that players at higher levels, including the number of

strokes per minute. Real play time and resting time in adolescent players was not

different between sexes and the adolescent players showed approximately, the

same number of strokes per rally but with a greater average duration compared

with adult elite athletes, determined by physical conditioning and technique

development (Kovacs, 2006).

1.4. The young tennis player

Studies about skeletal maturation in youth tennis players are still limited, and are

mainly related to growth status (Malina, 1994; Myburgh, et al., 2016a) or maturity-

associated variation in functional characteristics (Myburgh et al., 2016b) To date

no studies comparing methods were performed in a sample of young tennis

players, regarding the concurrent determination of skeletal age (SA), although this

was already done for other sports such as soccer (Malina et al, 2007).

Skeletal age is often considered the best index for the assessment of

biological maturation (Malina et al., 2004 ; Baxter-Jones et al., 2002). It is

established that skeletal age did not differ between active and inactive boys

followed longitudinally from 13 to 18 years (Beunen et al., 1992; Malina, 1994;

Beunen & Malina, 2007). The hypothesis about the interrelationship of variation in

16

maturity status related to body size, physique and body composition is often

assumed to success in sport career (Malina et al., 2004). Boys who were advanced

in maturity tended to be stronger and to perform better on motor skills than the

boys of de same chronological age who were on time or later in maturity (Malina et

al., 2004; Baxter-Jones et al., 1995). This issue was well documented in soccer

(Malina et al., 2000; Hirose, 2009; Figueiredo et al., 2009; Carling et al., 2009;

Teixeira et al., 2015).

A previous study (Malina, 2011) summarized the evidences about skeletal

age and maturity status in different sports, and it was possible to note a reduction

in the proportion of late maturing male athletes as adolescence progressed. The

authors hypotesized that this reflected the exclusive nature of sports and selective

dropout phenomenon (Pearson et al., 2006; Reilly et al., 2003; Myburgh et al., 2016

a; Myburgh et al., 2016 b). This was of special relevance, since maturation was

considered a major confounding variable in talent identification in adolescence

and has important implications to long-term developmental models of sport

preparation.

1.5. Skeletal age

The assessment of SA is used to estimate the level of skeletal maturity attained by a

youngster at the time of observation, (Malina et al., 2004; 2011). FELS and TW3

methods used standard radiographs of the hand wrist skeleton (radius, ulna,

carpals, metacarpals and phalanges). They were based on different and specific

criteria for each bone, basically shape differentiation, epiphysis union and the

17

attainment of the adult morphology. Therefore both methods were not considered

as equivalents and differed in SA results within an individual (Malina, 2011) and

this could be also among groups of different populations (Malina et al., 2004).

Skeletal age or also called bone age, in clinical issues is used to assess

skeletal development that is believed to be universal to all subjects in terms of

stages and sequences of changes. The measurement is taken against a standard

which consists of data of the skeletal development of normal and healthy children.

In other words, skeletal age corresponds to the chronological age at which the

children on whom the standard was based usually attained that same degree of

skeletal development. In clinical, it has been used basically two methods Greulich

and Pyle and TW2 (Flores et al., 2005), have been used and compared with ethnic

variation (Zhang et al., 2008; Kim et al., 2015), nutrition and endocrine system.

(Flores et al., 2005; Flor-Cisneros et al., 2006) and comparison of different

methods (Bull et al., 1999; Kim et al., 2015).

On the other hand, sport sciences also also use methods to estimate skeletal

age (Malina, 1971). Short-term longitudinal studies in several sports indicated

similar gains in skeletal maturation in both athletes and non athletes (Baxter-Jones

et al., 2002) variety of books and research describe about the potential influence of

variation in maturity status on size, physique and body composition (Beunen et al.,

1992; Armstrong, 2013; Malina et al., 2004; 2011; 2014), and definitely on

performance in sport.

18

In sport, it has been used three methods Greulich and Pyle, TW (Version 2

and 3), and Fels Method. Studies commonly examine young athletes contrasting in

maturity status within a particular age groups. Difference between skeletal age

and chronological age are used to classify participants as early maturing, average

or late maturity (Malina, 2011).

Specifically in tennis. maturity-related variation in functional

characteristics were recently described (Malina, 1994; Myburgh et al., 2016 b). The

authors found that advanced maturity in male players provided advantages in

specific motor skills like grip strength, speed, upper and lower body power, agility

and aerobic endurance.

The FELS method assigned SAs and was developed based on longitudinal

records of 355 boys and 322 girls in the Fels Longitudinal Study between 1932 and

1977. The sample was from middle-class families in south-central Ohio, USA. It

used 20 bones, plus the presence or absence of the pisiform and adductor

sesamoid, SA and standard error were calculated by a program application (Roche

et al., 1988). On the other hand, the TW3 method assigned maturity scores that

were converted to SAs. This method had two major revisions. The protocol was

based on British Children (~2600) from a home for children and public schools;

most were born between 1940 and 1955. The last revision occurred in 2001. The

criteria for the stages of maturation of 13 long bones emerged from samples of

European (British, Belgian, Italian, Spanish), Argentine, Japanese and well off

American youth. (Tanner et al., 2001).

19

1.6. Research question

Each method had been noted as having advantages and disadvantages. Differences

in protocols or their application illustrated variation among methods (Malina et al.,

2004; 2011). The current study was aimed to examine the agreement between

concurrent protocols for determination of skeletal age (FELS and TW3) in a sample

of male tennis players.

20

CHAPTER 2:

METHODS

2.1. Procedures

Informed consent was obtained from parents. The participation of the players was

voluntary and the sports entities were also informed about the whole experimental

procedure. Month of birth, height, sitting height body mass were measured,

anthropometry involved the use of some references defined for the

standardization of measurement procedures, A single anthropometrist measured

height, sitting height body mass in each country; Height was measured to the

nearest 0.1 cm using a calibrated Harpenden stadiometer fixed to a wall following

standardized procedures. Chronological age was calculated as the year between

the date of birth and the day of the x-ray examination, the radiograph was taken by

an onsite trained and certified technician in each country.

2.2. Participants

The sample is composed of 80 male tennis players aged 8.69-16.79. They were

from Great Britain (n=50), Portugal (n=17) and Colombia (n=13).

21

2.3. Skeletal age

Standard hand wrist radiograph was taken to estimate skeletal age with the

protocols to Fels method (Roche et al.,1988) and TW3 method from radio, ulna and

shorts bones RUS (Tanner et al., 2001).

2.4. Skeletal maturation

Mature state was obtained considering FELS method and the fact that a skeletal

age of 18.0 years corresponds to the mature state. The respective classification for

TW3 is 16.5 years (score=1000) for males.

2.5. Skeletal maturity

Players who reached skeletal maturity were labeled mature . Difference between

skeletal age and chronological age were used to each subject to classified in

average, early and late maturity. (Malina 2011).

Average= skeletal age within ± 1 year chronological age.

Early= Skeletal age older than chronological age by more than 1 year.

Late= skeletal age younger than chronological age by more than 1 year.

22

2.6. Statistical analysis

Descriptive statistics were calculated for the total sample (mean, standard

deviation, standard error and confidence interval 95% CI) to chronological age,

stature of the father, stature of the mother and anthropometry, equally descriptive

statistics for maturity offset, estimated age at the peak height velocity, predicted

mature (according to concurrent protocols) and skeletal aged determined by

concurrent protocols.

Analysis of variance ANOVA was used to compare Fels and TW3 SAs in

players who were not classified as skeletally mature by either method, the

Statistical Program for Social Sciences – SPSS, version 24.0 was used.

Finally, different linear relationships were plotted to CA, stature, body mass

and also to concurrent SAs protocols, using the graphpad prism version 5.0.

23

CHAPTER 3:

RESULTS

Descriptive statistics for the total sample to chronological age, stature of the father,

stature of the mother and anthropometry are summarized in table 1.

Mean of chronological age is 12,91 years, stature 159,86cms and body

mass 49,70kgs. The sitting height mean is taller than estimated leg length mean.

Descriptive statistics for maturity offset, estimated age at the peak height velocity,

predicted mature stature (according to different concurrent) and skeletal aged

determined by concurrent protocols are summarized in table 2.

Mean of skeletal age determined by TW2 is greater than means of TW3

and Fels methods, and Fels SA mean is greater than TW3 SA. Among players who

were not skeletal mature by either method (n= 72) The difference between mean

Fels and TW3 SAs are significant (F=37,50 , p<0,001).

Mean of predicted mature stature K&R is greater than mean of predicted

mature stature K&G in the total of the sample (n=80), the difference between

mean K&R and K&G is significant (t =3.682 , p<0,001).

24

Table 1. Descriptive statistics for chronological age, stature of the father, stature of the mother and anthropometry (n=80).

Variable

Unit Mean Standard

deviation Value SE (95%CI)

Chronological age Year 12.91 0.21 (12.49 to 13.33) 1.87

Stature of the father Cm 178.78 0.91 (176.95 to 180.60) 8.20

Stature of the mother Cm 165.19 0.69 (163.79 to 166.58) 6.25

Mid parental stature Cm 171.98 0.61 (170.76 to 173.20) 5.48

Stature Cm 159.86 1.55 (156.77 to 162.95) 13.88

Sitting height Cm 82.80 0.80 (81.20 to 84.40) 7.18

Estimated leg length Cm 77.05 0.81 (75.42 to 78.68) 7.31

Sitting height to stature ratio % 51.81 0.14 (51.52 to 52.11) 1.32

Body mass Kg 49.70 1.52 (46.66 to 52.74) 13.66

25

Table 2. Descriptive statistics for maturity offset, estimated age at the peak height velocity, predicted mature stature (according to different concurrent) and

skeletal aged determined by concurrent protocols (n=80).

* K&R (kamis & Roche), K&G (kamis & Guo)

Variable

Unit Mean Standard

deviation Value SE (95%CI)

Maturity offset year -0.64 0.19 (-1.04 to -0.25) 1.78

Age of peak height velocity year 13.56 0.07 (13.42 to 13.70) 0.64

Predicted mature stature (K&R) cm 181.77 0.75 (180.28 to 183.27) 6.72

Predicted mature stature (K&G) cm 180.45 0.82 (178.82 to 182.09) 7.34

Skeletal age (Fels) year 13.37 0.30 (12.76 to 13.99) 2.76

RUS # 558.85 26.06 (505.89 to 611.81) 238.00

Skeletal age (TW-Version 2) year 13.45 0.29 (12.86 to 14.04) 2.51

Skeletal age (TW-Version 3) year 12.85 0.27 (12.30 to 13.40) 2.48

26

Classification of skeletal maturity status by different protocols and

crosstab of absolute frequencies is shown in Table III. The totals to TW version 2

are late (n=7) on time(n=30) early (n=35) mature (n=8), to TW version 3 are late

(n=18), on time(n=41) early (n=13) mature (n=8) and Fels are late (n=10) on

time(n=35) early (n=33) mature (n=2). The method that most ranked late and on

time was TW version 3 (n=18 and n=41)respectively ; early was TW version 2

(n=35) and mature were TW version 2,3(n=8).

Two players are skeletally mature with the Fels method (CA 16,8 and 15.7

years), while eight are skeletally mature with the TW version 2 and 3 (CA 15.0–

16.8 years). The two players matures with Fels also are matures with TW method

in both versions.

Linear regressions for stature to CA and body mass to CA were plotted in

figures 1 and 2. Coefficients of correlation between stature and CA, body mass and

CA were r=0.83 and r=0.79 respectively.

27

Table 3. Skeletal maturity status* by different protocols and crosstab of absolute frequencies (n=80).

*Biological maturation is used for skeletal age and biological maturity status for categories derived from differences between chronological and skeletal ages.

Fels TW2

Late On time Early Mature Total Late On time Early Mature Total

TW2 Late 5 2 0 0 7

On time 5 20 5 0 30

Early 0 13 22 0 35

Mature 0 0 6 2 8

Total 10 35 33 2 80

TW3 Late 8 10 0 0 18 7 11 0 0 18

On time 2 23 16 0 41 0 19 22 0 41

Early 0 2 11 0 13 0 0 13 0 13

Mature 0 0 6 2 8 0 0 0 8 8

Total 10 35 33 2 80 7 30 35 8 80

28

Figure 1. Linear relationship between stature and CA for the total sample (n=80).

Figure 2. Linear relationship between body Mass and CA for the total sample (n=80).

Lineal regressions for SAs by concurrent protocols and CA were plotted in figures

3, 4 and 5.

29

Figure 3. Linear relationship between Fels and CA for the total sample (n=80).

Figure 4. Linear relationship between TW3 and CA for the total sample (n=80).

30

Figure 5. Linear relationship between TW2 and CA for the total sample (n=80).

Coefficients of correlation between Fels and CA were r=0.90, TW3 and CA

were r=0.89, TW2 and CA were r=0.87.

Linear regressions between concurrent SAs protocols were plotted in

figures 6, 7 and 8. Coefficients of correlation between TW version 3 and Fels were

r=0.90, between TW version 2 and Fels were r=0.93, between TW version 2 and

TW version 3 were r=0.98

31

Figure 6. Linear relationship between TW3 and Fels for the total sample (n=80).

Figure 7. Linear relationship between TW2 and Fels for the total sample (n=80).

32

Figure 8. Linear relationship between TW3 and TW2 for the total sample (n=80).

33

CHAPTER 4:

DISCUSSION

4.1. Main findings

Poor agreement between protocols Fels skeletal ages and TW3 skeletal ages in this

sample of male adolescent tennis players could be by different factors ; there are a

large inter-individual differences in skeletal maturations among youth (Malina et

al., 2004; Malina 2011). British, Portuguese and Colombian are a varied ethnic

sample, Therefore some players have been more affinity in a different protocol,

mainly in TW3 because the method in this version was based on samples of Europe

(British, Belgian, Italian, Spanish), South America(Argentinean) American and

Japanese children and adolescents (Malina et al., 2004); in contrast Fels protocol

was based in only American sample (Roche et al., 1988; Malina et al., 2004). and

does not fit a this sample.

4.2. Greulich and Pyle method

The Atlas of Skeletal Maturation of the Hand was published by Todd In 1937. It

started at the Western Reserve University School of Medicine in Ohio in 1929. Few

years later, Greulich and Pyle published ,in 1950, their atlas partly based on the

atlas by Todd. They were able to use all the radiographs obtained in the original

study. The Greulich and Pyle method as originally described involves a complex

34

comparison of all bones in the hand and wrist against reference standard

radiographs of different ages. A total of thirty bones need to be examined. The sex

of the patient is considered. Changes for males and females are described using

standard radiographic images of the left hand of children ordered by chronological

age.

The first step in the analysis compares the given radiograph with the image

in the atlas that corresponds to the chronological age of the patient. Afterwards,

one should compare the film with adjacent images representing both younger and

older children. When comparing the radiograph against an image in the atlas there

are certain features as maturity indicators. These features vary with the age of the

child, the presence or absence of certain carpal or epiphyseal, ossification centers,

the shape of the epiphyses and the amount of fusion with the metaphysic. (Malina

1971; Malina et al., 2004; 2011; Niemeijer, 2002).

4.3. Tanner and Whitehouse method (TW)

Tanner and Whitehouse contacted with the Greulich and Pyle atlas. They noticed

several aspects of the method and improved. The TW method had two important

revisions TW1 emerged from a sample of about 3000 healthy British children and

the method was matching features of 20 bones of the hand and written criteria for

the stages through which each bone passes in its progress from initial appearance

to mature state. These bones included the wrist, carpals, the thumb and the third

and fifth fingers. A specific point score is assigned to each stage of each bone, and

35

after the total of score are summed to a skeletal maturity score and finally to

converter the score to a skeletal age.

The score system changed in the revised TW2. It contains the same bones

and it was possible to have a score system from the radio, ulna and short bones

(RUS of 13 bones or carpal). The stage corresponding to a reference image is

assigned to each bone. When the stage of a certain bone is known the score

associated is retained to obtain a final score. After all the required bones have been

analyzed, the total score is determined and a certain skeletal age obtained via a

conversion table from score into skeletal age.

The most recent version TW3 (Tanner et al., 2001) provide RUS and carpal

SAs. It emerged from samples of British, Belgian, Italian, Spanish, Argentine,

American and Japanese children and adolescence. The criteria for assessing the

maturity status have not been changed, only de scale for converting the skeletal

maturity score to an SA (Malina, 1971; Malina et al., 2004; 2011; Niemeijer, 2002).

4.4. Fels method

The FELS method assigns SAs and uses the same 20 bones as the TW2 plus the

presence or absence of the pisiform and adductor sesamoid. It was developed

from longitudinal records of 355 boys and 322 girls in the Fels Longitudinal Study

between 1932 and 1977 and published in 1988. The sample was from middle-

class families in south-central Ohio, USA. SA and standard error are calculated by a

36

program system associated with each assessment. This error provides a degree of

confidence regarding the predicted values.

Different maturity indicators are involved in assessment at different

chronological ages. The values for the measured (epiphysis and metaphysis

widths) and assigned grades for specific shape bones are also maturity indicators.

(Roche et al., 1988; Malina et al., 2004).

4.5. Agreement among protocols

Although skeleton is a indicator of maturity and both protocols are based in the

same principles as they are; standard radiographs on the hand and wrist, shapes

and fusions of skeletal progress from cartilage to bone, that means from initial

formation towards adult morphology (Malina et al., 2004; Malina 2011); the same

radiographs can be two different skeletal ages by Fels and TW3 because they aren’t

equivalents, as well as in Fels 18.0 years is skeletal mature state and TW3 RUS is

score 1000, 16.5 years. Also, between protocols exist some differences in the

assessment , TW3 use (radius, Ulna, and short bones) RUS and transform a score to

get later a skeletal age (Tanner et al., 2001) whereas Fels use use 20 bones

(radius, ulna, metacarpals and falanges of the first, third and fifth digits) presence

or absence of the pisiform and adductor sesamoid assigning grades for bones

shapes, to get directly a skeletal age. (Roche et al., 1988; Malina et al., 2004).

According to Malina et al., (2004) exist disharmony in maturity of bones and this is

a genetic, but there is a inclination to reach a mature state first in carpals and in

late adolescence radius, ulna, metacarpals and phalanges.

37

4.6. Growth status

Stature and weight of this sample are below of P 50 like samples of adolescent

tennis players presents in different studies of growth state (Malina, 1994;

Myburgh et al., 2016a). Although this is a total, result was not separated by

chronological age of each country group, it means, the statures and weights of

British are not separated of Portuguese and Colombian sub samples, but it is

assumed that the Colombians could be a little lower scores in stature and weight.

4.7. Maturity status

Moreover, to this study the mean of Fels skeletal age (13,37 years) is greater to

mean of TW3 skeletal age (12,85 years) and mean of chronological age (12,91

years), this indicates that in Fels method there is a slight more advanced

classification. On the other hand TW3 has more late, on time and mature ranked

players than Fels, but Fels more early ranked players. These results are similar

with the results of the study with a sample of 40 elite Spanish soccer players

(Malina et al. 2007) where the TW3 ranked more players in mature state than Fels

method. In that study Fels skeletal ages are consistently in advance of TW3

Skeletal ages but kappa coefficients indicate moderate agreement between

maturity classifications based on the two methods of the assessments, in contrast

to this sample who indicate a fair agreement.

38

4.8. Interrelationship among indicators of biological maturation

Differences between skeletal age with other methods to assess maturation as they

are sexual and somatic are explained in (Malina et al., 2004) where exist some

differences in the results and in the classifications by maturity status, like the

method of maturity offset proposed by (Mirwald et al., 2002) has recently been

discussed, mainly by the influence with chronological age and a possible higher

agreement with boys who are on time in maturity status than with boys who are

late or advanced in maturity status, as well as to be a more effective method during

the interval of the growth spurt, 12-15 years in males (Malina & Kozieł 2014).

However to date, a few studies comparing methods of concurred protocols to

estimate skeletal age in sports, with aim of to explore differences in the youth

samples, but also to explore differences between protocols to assess and therefore

also in the results of the skeletal ages.

Skeletal age has an important utility in research with youth athletes and

impact the selection, development and progression in talent (Pearson et al., 2006;

Reilly et al., 2003); The same is exposed in studies with tennis (Myburgh et al.,

2016 a; Myburgh et al., 2016 b). The small absence of late maturing as adolescence

progresses which are evident in (Malina, 2011), it could be the result of a selective

dropout. Competitive youth tennis needs physical conditioning like speed, agility

and aerobic capacity to competition (Kovacs, 2007), and morphological conditions

like stature and long arms, hence, could be affect by the potential influence of

variation in maturity status on size, physique and body composition explained by

39

different authors (Malina et al., 2004; Baxter Jones et al., 1995); Although it

continue publishing researches of physical performance and anthropometric

characteristics in youth tennis players without taking into account the biological

maturation (Fernandez-Fernandez et al,. 2015;2016; Kramer, et al., 2016; Sánchez-

Muñoz et al., 2007).

Agreement between protocols Fels skeletal ages and TW3 skeletal ages is a

study related with talent identification, selection and development to have a

important practice with the long term training and a special reference on the

framework to growth and maturation. This study Included for the first time a

sample of elite Colombian tennis players.

40

CHAPTER 5:

CONCLUSIONS

The present study examined the agreement between concurrent protocols for

determination of skeletal age (FELS and TW3) in a sample of male tennis players.

Mean of skeletal age determined by Fels was greater than TW3 SA. Among players

who were not skeletal mature by either method the difference between mean Fels

and TW3 SAs was significant. Classification of skeletal maturity status by the two

protocols had more frequencies in TW3 “on time” and “late” status than Fels, this

indicates that in Fels method there is a slight more advanced classification.

The limitations of this master dissertation were to have a small sample,

different number in proportion of the sample from each country, results were not

separated by chronological age of each country group, it means, British are not

separated of Portuguese and Colombian sub samples, but it is assumed that scores

can be different in each sub sample.

41

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